Intuitively, the high accuracy and low uncertainty of the forecasts are critical insights for uncovering the influence of external shocks and events on large-scale time series data [1]}.
To provide sustainable economic development and resilience planning, it is crucial to understand how different industries are influenced by and recover from such extreme events over time [2]}.
However, it remains a challenge to develop reliable and accurate prediction models as the real-world dataset often contains anomalies that tend to be rare and random. Hence, it is crucial to develop a forecast model that can leverage previously seen extreme events and anomalies for future forecasts.
